Compressed Sensing of EEG for Wireless Telemonitoring with Low Energy Consumption and Inexpensive Hardware

Sometimes you focus on developing hardware, sometimes, you work around the hardware, Zhilin Zhang sent me the following:

Dear Igor, 

...Thank you for your blog, which plays an important role in my research. Recently we have a paper accepted by IEEE Trans.on Biomedical Engineering. The title is "Compressed Sensing of EEG for Wireless Telemonitoring with Low Energy Consumption and Inexpensive Hardware". The link is here: http://arxiv.org/abs/1206.3493. The code (with data) has been incorporated into the BSBL package (http://dsp.ucsd.edu/~zhilin/BSBL_public.zip). In this work we apply BSBL to wireless telemonitoring of EEG. Although EEG is not sparse, the recovery quality by BSBL is satisfactory, which can meet the requirement of regular EEG analysis. This preliminary work is our first step toward smart-phone based brain-computer interface.  I very appreciate if you can announce the paper in your blog.

Best regards,

Zhilin

[my emphasis] 

Thanks Zhilin

Here the paper: Compressed Sensing of EEG for Wireless Telemonitoring with Low Energy Consumption and Inexpensive Hardware by Zhilin Zhang, Tzyy-Ping Jung, Scott Makeig, Bhaskar D. Rao. The abstract reads:

Telemonitoring of electroencephalogram (EEG) through wireless body-area networks is an evolving direction in personalized medicine. Among various constraints in designing such a system, three important constraints are energy consumption, data compression, and device cost. Conventional data compression methodologies, although effective in data compression, consumes significant energy and cannot reduce device cost. Compressed sensing (CS), as an emerging data compression methodology, is promising in catering to these constraints. However, EEG is non-sparse in the time domain and also non-sparse in transformed domains (such as the wavelet domain). Therefore, it is extremely difficult for current CS algorithms to recover EEG with the quality that satisfies the requirements of clinical diagnosis and engineering applications. Recently, Block Sparse Bayesian Learning (BSBL) was proposed as a new method to the CS problem. This study introduces the technique to the telemonitoring of EEG. Experimental results show that its recovery quality is better than state-of-the-art CS algorithms, and sufficient for practical use. These results suggest that BSBL is very promising for telemonitoring of EEG and other non-sparse physiological signals.

and the attendant code.

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